Neural Computations in Visual Cortex

描述(申请人提供)：本研究的目标是了解初级视觉皮质(V1)进行的计算的性质，以及这些计算是如何进行的。即使是解释视觉世界的最基本步骤--提取线条和边缘等局部特征--也是一个困难的计算问题：它必须在杂乱、复杂、自然的视觉场景中进行；必须快速进行；并且必须通过神经硬件进行。普遍接受的观点是，V1主要起到前馈滤波器组的作用，其中反馈和增益控制起到调节作用。然而，由简单的分析方便的刺激构建的模型提供了对自然场景的反应的不完整的解释。由于自然场景具有传统分析刺激所缺乏的特征，这一观察表明V1神经元对这些区分特征，即高阶统计量(HOS)很敏感。根据几条证据(包括前一资金时期的工作和其他实验室的研究)，我们假设这种对居屋的敏感性表明，V1的S基本设计是一个强递归网络的设计。特别是，我们假设V1的强循环结构与前馈或调制反馈结构的区别特征解释了V1的S提取HOS的能力。为了检验这些假设，我们集中分析了V1的S对包含HOS的刺激的反应--因为它们区分了V1的这两幅对比图，也因为HOS正是区分自然场景和传统分析刺激的统计特征。在目标1中，我们确定了V1神经元对HOS的敏感程度，明确地研究了人工构建的刺激和来自自然场景的刺激。在目标2中，我们通过分析多神经元放电模式的统计数据，确定神经集合体的动态形成是否是提取HOS的基础。如果成功，这项工作将为V1的设计原则提供基本的见解，包括它如何利用大脑皮层结构的一般特征来进行视觉所需的计算，稀疏表示是如何产生的，以及大脑皮层神经“噪音”的功能意义。 与公共健康相关：该项目的长期目标是了解大脑如何分析传入的视觉信息。对这一过程的深入了解将提高我们诊断和修复感知和认知功能障碍的能力，这些障碍会在弱视、自闭症、阿尔茨海默病、中风和慢性脑损伤等不同的疾病中导致显著的发病率。

DESCRIPTION (provided by applicant): The goal of this research is to understand the nature of the computations performed by primary visual cortex (V1), and how these calculations are carried out. Even the most basic step in interpreting the visual world -- extracting local features such as lines and edges -- is a difficult computational problem: it must be carried out in the context of cluttered, complex, natural visual scenes; it must be carried out rapidly; and it must be carried out by neural hardware. The generally accepted view is that V1 acts primarily as a feedforward bank of filters, in which feedback and gain controls play a modulatory role. However, models constructed from simple analytically-convenient stimuli provide an incomplete account of responses to natural scenes. Since natural scenes have characteristics that traditional analytic stimuli lack, this observation implies that V1 neurons are sensitive to these distinguishing characteristics, namely, high-order statistics (HOS's). Based on several lines of evidence (including work from the previous funding period and studies in other laboratories), we hypothesize that this sensitivity to HOS's indicates that V1's basic design is that of a strongly recurrent network. In particular, we hypothesize that the characteristics that distinguish a strongly recurrent architecture from a feedforward or modulatory feedback architecture account for V1's ability to extract HOS's. To test these hypotheses, we focus on analyzing V1's responses to stimuli containing HOS's -- because they distinguish among these two contrasting pictures of V1, and because HOS's are precisely the statistical feature that distinguishes natural scenes from traditional analytic stimuli. In Aim 1, we determine the extent of sensitivity of V1 neurons to HOS's, explicitly studying both artificially- constructed stimuli and stimuli derived from natural scenes. In Aim 2, we determine whether dynamic formation of neural assemblies underlies the extraction of HOS's, by analyzing the statistics of multineuronal firing patterns. If successful, this work will provide fundamental insights into the design principles of V1, including how it exploits general features of cortical architecture to carry out the calculations necessary for vision, how sparse representations arise, and the functional significance of cortical neural "noise." PUBLIC HEALTH RELEVANCE: The long-term goal of this project is to understand how the brain analyzes incoming visual information. An enhanced understanding of this process will advance our ability to diagnose and remediate disturbances of perception and cognitive function, which cause significant morbidity in conditions as disparate as amblyopia, autism, Alzheimer's Disease, stroke, and chronic brain injury.